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Found 6 papers, 6 papers with code
Intra-class Feature Variation Distillation for Semantic Segmentation
In this paper, different from previous methods performing knowledge distillation for densely pairwise relations, we propose a novel intra-class feature variation distillation (IFVD) to transfer the intra-class feature variation (IFV) of the cumbersome model (teacher) to the compact model (student).
Affinity Space Adaptation for Semantic Segmentation Across Domains
Specifically, we perform domain adaptation on the affinity relationship between adjacent pixels termed affinity space of source and target domain.
Learning Directional Feature Maps for Cardiac MRI Segmentation
Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters.
Gliding vertex on the horizontal bounding box for multi-oriented object detection
Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts.
Ranked #42 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
TextField: Learning A Deep Direction Field for Irregular Scene Text Detection
Experimental results show that the proposed TextField outperforms the state-of-the-art methods by a large margin (28% and 8%) on two curved text datasets: Total-Text and CTW1500, respectively, and also achieves very competitive performance on multi-oriented datasets: ICDAR 2015 and MSRA-TD500.
Ranked #22 on
Scene Text Detection
on Total-Text
DeepFlux for Skeletons in the Wild
In the present article, we depart from this strategy by training a CNN to predict a two-dimensional vector field, which maps each scene point to a candidate skeleton pixel, in the spirit of flux-based skeletonization algorithms.
Ranked #1 on
Object Skeleton Detection
on SK-LARGE
